ALLUXIO WEBINAR
With data lakes expanding from on-prem to the cloud as well as increasing use of new object data stores, data platform teams are challenged with providing consistent, high-throughput access to distributed data sources for analytics and AI/ML applications. In today’s hybrid cloud and multi-cloud era, data-intensive applications such as Presto, Spark, Hive, and Tensorflow are suffering more sluggish response times and increased complexity with the growing separation of data and compute.
Join Alluxio’s distributed systems experts as they explore today’s data access challenges and open source data orchestration solutions for modernizing your data platform.
In this tech talk, you’ll learn:
- How data access and throughput challenges are hindering large-scale analytics and AI/ML applications
- How a data orchestration layer can simplify distributed data access and improve performance
- Real-world production use cases and example journeys for architecting a modern data platform
ALLUXIO WEBINAR
With data lakes expanding from on-prem to the cloud as well as increasing use of new object data stores, data platform teams are challenged with providing consistent, high-throughput access to distributed data sources for analytics and AI/ML applications. In today’s hybrid cloud and multi-cloud era, data-intensive applications such as Presto, Spark, Hive, and Tensorflow are suffering more sluggish response times and increased complexity with the growing separation of data and compute.
Join Alluxio’s distributed systems experts as they explore today’s data access challenges and open source data orchestration solutions for modernizing your data platform.
In this tech talk, you’ll learn:
- How data access and throughput challenges are hindering large-scale analytics and AI/ML applications
- How a data orchestration layer can simplify distributed data access and improve performance
- Real-world production use cases and example journeys for architecting a modern data platform
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Fireworks AI is a leading inference cloud provider for Generative AI, powering real-time inference and fine-tuning services for customers' applications that require minimal latency, high throughput, and high concurrency. Their GPU infrastructure spans 10+ clouds and 15+ regions, serving enterprises and developers deploying production AI workloads at scale.
With model sizes reaching 70GB+, Fireworks AI faced critical challenges: eliminating cold start delays, managing highly concurrent model downloads across GPU clusters, reducing tens of thousands in annual cloud egress costs, and automating manual pipeline management that consumed 4+ hours weekly. They chose Alluxio as their solution to scale with their hyper-growth without requiring dedicated infrastructure resources.
In this tech talk, Akram Bawayah, Software Engineer at Fireworks AI, and Bin Fan, VP of Technology at Alluxio, share how Fireworks AI uses Alluxio to power their multi-cloud inference infrastructure.
They discuss:
- How Fireworks AI uses Alluxio in its high-performance model distribution system to deliver fast, reliable inference across multiple clouds
- How implementing Alluxio distributed caching achieved 1TB/s+ model deployment throughput, reducing model loading from hours to minutes while significantly cutting cloud egress costs
- How to simplify infrastructure operations and seamlessly scale model distribution across multi-cloud GPU environments
